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As a vital ecological barrier in China, Yellow River Basin (YRB) is strategically significant for China's national development and modernization. However, YRB has fragile ecosystems, and is sensitive to climatic change. Extreme climate events (e.g., heavy precipitation, heatwaves, and extreme hot and cold) occur frequently in this basin, but the implications (positive and negative effects) of these events on vegetation dynamics remains insufficiently understood. Combing with net primary productivity (NPP), the normalized difference vegetation index (NDVI) and extreme climate indexes, we explored the spatio–temporal characteristics of plants' growth and extreme climate, together with the reaction of plants' growth to extreme climate in the Yellow River Basin. This study demonstrated that annual NPP and NDVI of cropland, forest, and grassland in the study region all revealed a climbing tendency. The multi-year monthly averaged NPP and NDVI were characterized by a typical unimodal distribution, with the maximum values of NPP (66.18 gC·m−2) and NDVI (0.54) occurring in July and August, respectively. Spatially, multi–year averaged of vegetation indicators decreased from southeast to northwest. During the study period, carbon flux (NPP) and vegetation index (NDVI) both exhibited improvement in most of the YRB. The extreme precipitation indexes and extreme high temperature indexes indicated an increasing tendency; however, the extreme low temperature indexes reduced over time. NPP and NDVI were negatively associated with extreme low temperature indexes and positively correlated with extreme high temperature indexes, and extreme precipitation indicators other than consecutive dry days. Time lag cross–correlation analysis displayed that the influences of extreme temperature indexes on vegetation indexes (NPP and NDVI) were delayed by approximately six months, while the effects of extreme precipitation indexes were immediate. The study outcomes contribute to our comprehension of plants' growth, and also their reaction to extreme climates, and offer essential support for evidence–based ecological management practices in the Yellow River Basin. [ABSTRACT FROM AUTHOR]

Blue–green space refers to blue space (rivers and lakes) and green space (lawns and trees), which have the cooling island effect and are increasingly acknowledged as a potential and effective way to help alleviate the urban heat island effect. Scientific and flexible blue–green space planning is required, especially for medium- and large-scale urban agglomerations in the face of climate change. However, the temporal evolution and spatial patterns of the cooling island effect in the blue–green space under different future scenarios of climate change have not been fully investigated. This would impede long-term urban strategies for climate change adaptation and resilience. Here we studied the relationship between future climate change and blue–green spatial layout with Weather Research and Forecasting (WRF), based on the numerical simulation data of 15 global climate models under different extreme Shared Socioeconomic Pathway (SSP) scenarios. As a result, future changes in urban cooling island (UCI) magnitudes were estimated between historical (2015–2020) and future timelines: 2030s (2021–2040), 2050s (2041–2060), 2070s (2061–2080), and 2090s (2081–2100). Our results showed different land use types in blue and green space across the study area were predicted to present various changes in the next 80 years, with forest, grassland, and arable land experiencing the most significant land use transfer. The future UCI intensity of cities under SPP5-8.5 (12) was found to be lower than that under SPP2-4.5 (15), indicating that cities may be expected to experience decreases in UCI magnitudes in the future under SSP5-8.5. When there is no expansion of urban development land, we found that the conversion of different land use types into blue and green space leads to little change in future UCI intensity. While the area growth of forests and water bodies is proportional to the increase in UCI, the increase of farmland was observed to have the most significant impact on reducing the amplitude of urban UCI. Given that Huai'an City, Yancheng City, and Yangzhou City have abundant blue–green space, the urban cooling island effect was projected to be more significant than that of other cities in the study area under different SSP scenarios. The simulation results of the WRF model indicate that optimizing the layout of urban blue–green space plays an important role in modulating the urban thermal environment. [ABSTRACT FROM AUTHOR]

Globally the area of agricultural land is shrinking due to environmental degradation, market change forces or socio-political reasons. Restoration of degraded lands no longer used for agriculture may present a major conservation opportunity. Although expensive, restoring these areas should involve minimal social or political opposition and offer new economic opportunities and help mitigate climate change. Despite the benefits, how to reliably and accurately identify these lands over broad areas, along with their extent and potential role in biodiversity conservation, is an important question that remains to be solved. This project aims to build a novel framework, using remote sensing 'big data' techniques (e.g. Google Earth Engine) with large-scale, long time series and high resolution satellite data, as well as social and economic metrics, to automatically map uncontested lands worldwide and then assess their conservation potential. The results from this project will present the stock of uncontested lands, the costs and benefits of their restoration, which will support key government policy decisions that balance conservation goals with needs for food, energy and biodiversity. Our evaluation of the ecology and economics of restoring these lands can also guide investment decisions by conservation NGOs (e.g. The Nature Conservancy) and multilateral lenders like the World Bank. [ABSTRACT FROM AUTHOR]

Each year, terrestrial ecosystems absorb more than a quarter of the anthropogenic carbon emissions, termed as land carbon sink. An exceptionally large land carbon sink anomaly was recorded in 2011, of which more than half was attributed to Australia. However, the persistence and spatially attribution of this carbon sink remain largely unknown. Here we conducted an observation-based study to characterize the Australian land carbon sink through the novel coupling of satellite retrievals of atmospheric CO2 and photosynthesis and in-situ flux tower measures. We show the 2010-11 carbon sink was primarily ascribed to savannas and grasslands. When all biomes were normalized by rainfall, shrublands however, were most efficient in absorbing carbon. We found the 2010-11 net CO2 uptake was highly transient with rapid dissipation through drought. The size of the 2010-11 carbon sink over Australia (0.97 Pg) was reduced to 0.48 Pg in 2011-12, and was nearly eliminated in 2012-13 (0.08 Pg). We further report evidence of an earlier 2000-01 large net CO2 uptake, demonstrating a repetitive nature of this land carbon sink. Given a significant increasing trend in extreme wet year precipitation over Australia, we suggest that carbon sink episodes will exert greater future impacts on global carbon cycle. [ABSTRACT FROM AUTHOR]